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Exploring the Convergence of AI, Data and HPC

The demand for performant and scalable AI solutions has stimulated a convergence of science, algorithm development, and affordable technologies to create a software ecosystem designed to support the data scientist. A special insideHPC report explores how HPC and the data driven AI communities are converging as they are arguably running the same types of data and compute intensive workloads on HPC hardware, be it on a leadership class supercomputer, small institutional cluster, or in the cloud.

Outlook on Artificial Intelligence in the Enterprise 2018

Narrative Science, a leader in Advanced Natural Language Generation (Advanced NLG) for the enterprise, announced the availability of its third annual research report, “Outlook on Artificial Intelligence in the Enterprise 2018.” In partnership with the National Business Research Institute (NBRI), Narrative Science surveyed business executives from a wide array of functions, including business intelligence, finance, and product management, to understand the use, value, and impact of AI throughout their businesses.

Machine Learning in Finance: Challenges, Successes & Opportunities

AI or machine learning is changing the way industries across the spectrum interact with their customers, as well as develop their processes. And nowhere is this more evident than in the financial business. Download a new insideHPC special report that explores the benefits, challenges and considerations involved with adopting machine learning in finance.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – December 2017

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Study on AI and the Future of Sales

Whether it’s redefining the world of marketing, finance or customer support, it is no secret that artificial intelligence (AI) is changing the way we work. Sales, as both a function and a profession, is no exception to the sea change. Our friends over at Cien produced a global study: The Future of Sales that reveals how attitudes, perception and behavior of high tech sales professionals is not what is often touted in the media.

“Intelligent Storage” Market Survey Shows Growing Problem Moving Large Data Sets for a Variety of Applications

G2M Research, an analyst firm covering the Non-Volatile Memory Express® (NVMe) marketplace, released the results of its recent survey on the need for “Intelligence storage” for applications with large data sets. The survey, sponsored by NGD Systems, was conducted across 112 respondents from organizations involved in Big Data, artificial intelligence/machine learning, and Internet of Things (IoT) applications.

Research of 1,001 Data Scientist LinkedIn Profiles

Data science is a super-hot topic and the data scientist job is the sexiest job of the 21st century according to the Harvard Business Review. But how does one actually become a data scientist? 365 DataScience gathered data from 1,001 publicly listed LinkedIn profiles of data scientists and prepared a compelling report “Studying 1,001 Data Scientist LinkedIn Profiles.”

Nearly 40% of Data Professionals Spend Half of their Time Prepping Data Rather than Analyzing It

TMMData, creator of flexible data integration and preparation platform Foundation, partnered with the Digital Analytics Association to survey its community about data priorities and challenges. The survey revealed that data access, quality and integration present persistent, interrelated roadblocks to efficient and confident analysis across industries. Most notably, nearly 40% of data professionals (37.5%) spend more than 20 hours per week accessing, blending and preparing data rather than performing actual analysis.

Applying AI Technology to Reduce AML Risk for Global Financial Institutions

QuantaVerse published a new paper that examines how financial institutions can apply AI and machine learning technologies into their anti-money laundering (AML) ecosystems. For financial institutions, the time is now to deploy AI into their AML ecosystems. AI and machine learning hold the key to reducing risk related to financial crimes, addressing regulation, driving out operational cost through improved efficiency and, most importantly, effectively preventing criminals and terrorists from using the banking industry for their evil agendas.

Data 2020: State of Big Data Study

Our friends over at SAP recently published a study that highlights some really compelling findings around data scientists. There’s a wide gap and serious discrepancy in the level of importance organizations place on data scientists and the number of data scientists they employ. Below are some key statistics from the study, along with a summary infographic.